RoboDojo

This directory defines one public RoboDojo benchmark service. It owns the simulator schedule, public observations and actions, fixed robot-only execution, native scoring and evidence. Models never import or drive the native simulator.

Directory responsibilities

RoboDojo/
├── server.py                       # public launch entrypoint
├── server.example.json             # deployment configuration example
├── bench_server/                   # benchmark service package
│   ├── adapter.py                  # public contract to native environment bridge
│   ├── native_worker_client.py     # private worker communication
│   └── protocol/                  # service-owned evaluation definitions
│       ├── task_catalog.json       # task definitions and fixed panels
│       ├── task_catalog.py         # catalog loading and validation
│       └── robot_feedback.py       # public robot-control receipt schema
├── environment/                    # native worker, kinematics and runtime pins
└── checks/                         # benchmark contract and implementation tests

bench_server/ owns both the service implementation and its evaluation protocol. Its protocol/ subpackage remains dependency-light so catalog and reporting tools can read definitions without starting the server or simulator.

This is an abbreviated responsibility view; protocol guides and package marker files are omitted. The task catalog is benchmark protocol data: it defines instructions, seeds, horizons and fixed evaluation panels. The server reads it to freeze the schedule. Native assets and runtime bindings belong in environment/; operator paths, GPU selection and the chosen named panel belong in server.example.json or an operator-owned copy.

bench_server/native_worker_client.py connects the adapter to the private native worker. Model runners use the shared SIM service client instead. Moving these modules into packages does not change the wire protocol, action semantics, native scorer or historical result identities. The public launch command remains python -m benchmarks.simBenchmarks.RoboDojo.server.

A private worker timeout or malformed acknowledgement faults that transport: the service does not replay the operation or enqueue finalization behind an unknown result. Cleanup uses bounded child termination and retains the error and cleanup receipt. A complete, correlated native error can still permit read-only finalization. Neither outcome authorizes an automatic case retry.

Supported service

The server exposes Direct control with two explicit deployment identities: deployment_track=reference (the default) uses robodojo-direct-v2-rgb-proprio28-eef16-robot-feedback-v1; deployment_track=official uses robodojo-official-hf91f76c28-rgb-proprio28-eef16-feedback-v1. The official runtime binds public source and assets without claiming historical reference equivalence. These identities describe robot observations/actions and native execution, not the agent method. The current GPT driver defaults to generic codex-direct/v3; using it does not change this server interface or reproduce the historical v2 method's scores. The task catalog currently enables classify_objects with two immutable schedules:

fast: fixed standard layouts 0–4, each attempted once;
full: layouts 0–49, matching the upstream configured 50 episodes, each attempted once;
native horizon 1100 controls per layout;
three RGB cameras plus measured robot joints/end-effector poses;
absolute dual-arm end-effector targets;
fixed numerical DLS conversion followed by one native control;
one public robot-control acknowledgement after each executed control;
native task success and score remain evaluator-private until reporting.

There is no runtime selector for earlier joint-control, v1, no-ACK, or experimental arrange-number services. Their source remains recoverable from Git history and local ignored barks/; it is not part of this public entrypoint.

Ownership boundary

benchmarks/simBenchmarks/RoboDojo owns benchmark behavior. Model prompts, conversation state, voluntary-stop rules and action-generation logic belong in AgentPolicies/<model-profile>/. Public results belong in AgentPolicies/<model-profile>/results/robodojo/. The shared benchkit/ may validate and publish closed evidence, but it does not choose robot actions.

The service may expose declared robot state. It never exposes object truth, layout metadata, reward internals, success predicates, hidden scorer state or a task-solving RPC. Invalid public actions are not executed; the service returns a structured rejection so the client can make its next decision. An uncertain native or transport failure remains terminal.

Start the benchmark server

Copy server.example.json for the reference deployment or server.official.example.json for the official deployment to an operator-owned path and replace the runtime manifest path, digest and GPU. Protocol fields are not configurable. Select only a named catalog panel; arbitrary layout lists are rejected. Use a fresh server output directory and a strong evaluator-only token:

export RSI_SIM_SERVER_TOKEN='replace-with-a-url-safe-secret-of-at-least-32-characters'
python -B -m benchmarks.simBenchmarks.RoboDojo.server \
  --parameters /absolute/robodojo-direct-v2.json \
  --seed 20260907 --port 8765 \
  --output-dir /absolute/results-staging/robodojo/server

The service binds to loopback. Use authenticated SSH forwarding when the model runner is on another node. Startup freezes the schedule but does not reset the simulator. A case is claimed once; failed reset/action evidence is retained and the case is not automatically retried.

Run the model-owned client separately, for example:

python -B -m AgentPolicies.gpt6_astra.run \
  --sim-server http://127.0.0.1:8765 --benchmark robodojo \
  --control-method codex-direct/v3 \
  --seed 20260907 --episodes 5 --max-steps 1100 \
  --max-policy-calls 240 --replan-interval 5 --budget-mode horizon \
  --output-dir /absolute/results-staging/robodojo/client \
  --run-id classify_objects-category-sorting-direct-v3-panel5

The client command and credentials are model-owned. The benchmark server does not import AgentPolicies/gpt6_astra. The five-control prefix is a method configuration, not a server-side repeat rule; controls are submitted and acknowledged individually within the 1100-control horizon. By default, native videos remain in the server output and the client retains references. Use client --copy-server-media only when another local media copy is wanted; see recording options.

Evidence and status

Every case retains its outcome, counts, configuration and separate errors. Detailed interactions and videos are optional, enabled by default where supported; see recording options. The current server still generates native evidence; client retention switches do not disable that capture. New services keep the three server-side camera videos by default, with client references instead of duplicate downloads. Temporary frame images are not kept after successful close when video evidence is available; use server --retain-frame-images to retain them explicitly. Paired receipts, native events and all three videos support the existing full-evidence audit/publication path, not general participation or basic result eligibility. fast is the five-layout reference comparison panel; full is this task's 50-layout native evaluation schedule, not the whole multi-task RoboDojo matrix. CPU tests and artifact audits do not prove native rendering or task success. The latest recorded native panel and public report live under the model result namespace; generated media and raw evidence are intentionally outside Git and require durable artifact storage.